Full text: Mapping without the sun

Chao Mu a , Qin Yan b , Jie Yu a , Huiling Qin a 
a School of Remote Sensing Information Engineering, Wuhan University, Wuhan 430079,CHINA- (otto0127,yuj_2004) @126.com, 
qhlchch@ 163 .com 
b Chinese Academy of Surveying and Mapping, Beijing 100039,CHINA- 
KEY WORDS: Remote Sensing Image, Fractal Compression, Iterated Function System, Classification, Self-similarity 
The high compression ratio and excellent quality of fractal image compression have restricted the applications, due to the consuming 
encoding time. This paper discusses an improved method of the fractal remote-sensing image compression. The method is to divide 
the images and searching the iterated function system between the range blocks and the domain blocks quickly through analyzing the 
texture characters of the images. The goal is to identify the most similar range blocks and domain blocks among the blocks with same 
characteristic features. The test results demonstrated the quality and the speed of the remote-sensing images coding were improved 
with this method. 
The spatial resolution of remote sensing images is of 
unceasing enhancement; huge amount of data will require 
much more storage space and transmission time. In order to 
solve the problem of storage space and transmission time, the 
remote sensing images compression becomes one of the hot 
topics at present. 
Recently, the main image compression methods are the JPEG 
compression standard, the wavelet analysis compression and 
so on (Hongmei Tang, 2004). But the characteristics of the 
remote sensing images are different from the other general 
images; its content is mostly about natural surface, such as the 
water, vegetation, mountains, etc. These textures are often 
clear, and the content generally has the characteristic of the 
self-similarity. In this paper, a traditional fractal compression 
method applied in the remote sensing images is introduced. 
As the exhausted encoding time, an improved method base on 
the classification is discussed. It could be proved to lower the 
encoding time. 
The most famous characteristic of Fractal is the self-similar. It 
means no matter how changes of the scale of geometry, a 
small part of any image is extremely similar with the larger 
part ones. From the perspective of fractal image compression, 
it is to the most efficiently use of the self-similarity between 
the parts and blocks of image. The main point is to divide an 
image into several parts (range blocks), which aren’t 
overlapped; meanwhile, dividing the image into several 
blocks (domain blocks) that could overlap each other. Then 
for each range blocks R t , finding the most similar domain 
block D\ to match with them. At last, establishing the 
relationship between the range blocks and domain blocks: the 
domain block may infinite approach to the range block 
through the affine transformation, that is r * w. (z>.). For 
each range blocks, finding the Iterated function w j , range 
blocks are storied in the form of Iterated Function. The 
Iterated Function often can be expressed by only a few 
parameters, and according to the Collage Theorem, the 
iterated image (decoded image) has nothing to do with the 
original images. Therefore, the fractal compression can be 
achieved at a high compression ratio. When decoding the 
images, we need only iterate the Iterated Function parameters 
of each range blocks; the original images can be restored. 
Iterated Function System (listed IFS) is the important content 
of fractal theory. The basic idea is to identify geometric 
objects as a whole and partial, having the self-similar 
structure in the meaning of affined change. The most 
important theorem of fractal image compression is the 
Collage Theorem (Welstead, 1999). This theorem proves that 
for a range block we can always find the domain block, which 
is closest to the range block with a low erroneous distortion. It 
also manifested that the decoding image is nothing to do with 
the original image. So only saving every Iterated function w t , 
it can achieve the aim of compression (Yudong Fang, 1996). 
In this experiment, a 512 x 512 greyscale TM image, with 265 
grey levels for each pixels has been taken for being compressed

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